Short Coherent Processing Interval Research Articles

Two-dimensional reweighted alternating direction method of multipliers for inverse synthetic aperture radar imaging with pattern-coupled sparse structure

To realize inverse synthetic aperture radar imaging with limited bandwidth and short coherent processing interval, in view of pattern-coupled sparse structure of imaging targets, a two-dimensional joint sparse imaging algorithm, namely two-dimensional reweighted alternating direction method of multipliers (2D-RADMM), is proposed. The main idea of the proposed algorithm contains two aspects: one is expanding one-dimensional RADMM (1D-RADMM) to 2D-RADMM by matrix processing, so as to reduce the computation and memory usage. The other is designing different pattern-coupled modes to further improve the imaging performance. Extensive experiments based on two sets of measured data demonstrate that compared with traditional unweighted algorithms, the proposed algorithm has excellent imaging quality under noisy and sparse conditions, and its imaging time is <2 s, which can be suitable for the real-time inverse synthetic aperture radar imaging.

Eigenvalue‐based ground target detection in high‐resolution range profiles

In this study, the authors address the problem of range-distributed target detection in sequential high resolution range profiles (HRRPs). They propose a modified scaled largest eigenvalue detector for static target in homogenous ground clutter. A set of secondary data, which are free of target signal and have the same distribution as the clutter in the primary data, are assumed to be available. First, the sample covariance matrix (SCM) is estimated from the acquired multiple HRRPs in a short coherent processing interval. Then, the eigenvalue decomposition of the SCM is performed, and the eigenvalues are sorted in descending order. Finally, the largest eigenvalue scaled by the noise power estimated from the secondary data is selected as the detection statistic. Compared with existing methods of largest eigenvalue-based detection, the proposed method achieves better detection performance for coloured clutter by considering secondary data. Numerical and experimental results demonstrate the effectiveness of the proposed method.

A Novel Ship Imaging Method with Multiple Sinusoidal Functions to Match Rotation Effects in Geosynchronous SAR

Geosynchronous Synthetic Aperture Radar (GEO SAR) has a very long Coherent Processing Interval (in the order of hundreds of seconds) compared with other SAR platforms. Thus, the current methods of rotation effect matching and ship imaging that operate within a relatively short Coherent Processing Interval (in the order of seconds) are obviously not applicable. To address this problem, a novel ship imaging method with multiple sinusoidal functions matching for rotation effects is proposed for GEO SAR. Firstly, the influence of the rotational motion of a ship on the slant range is analyzed. It can be matched with the sum of multiple sinusoidal functions, and the signal model of a ship with rotational motion is given. Then, multiple sinusoidal functions for the matching-based ship imaging method are proposed, and their procedures are presented as follows: (1) The Generalized Keystone Transform and Generalized Dechirp Process (GKTGDP) is modified to compensate for the range migration and phase caused by the motion of GEO SAR. Then, the signal is focused at the frequencies of sinusoidal functions, and the frequencies can be matched. (2) From the matched frequencies, the other parameters of sinusoidal functions can be matched by parameter searching. (3) Based on the matched results, the Back Projection Algorithm (BPA) is used to take an image of the ship with rotational motion. Finally, the effectiveness of the proposed method is verified by numerical experiments.

레이다 모션에 따른 STAP 위상 보상

The conventional space-time adaptive processing (STAP) using a short coherent processing interval (CPI) is generally applied to an environment, where the variations in angle and Doppler information caused by stationary clutter patches during the CPI are negligible. The direct application of the conventional STAP to a relatively long CPI environment causes a performance degradation. This study proposes a method of applying the conventional STAP to the data acquired for a medium CPI scenario. In the proposed method, we compensate for the received signal phase by considering the change in the angle and Doppler information during the CPI, which results in the reduction of the phase variation due to the radar motion. Furthermore, we propose a method of improving the angular resolution by forming a virtual aperture when a high PRF radar with a non-negligible motion that satisfies the DPCA condition. Finally, we demonstrate the proposed method’s performance through a simulation.

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.

Bayesian 3‐D interferometric ISAR imaging for the targets with limited pulses

It is difficult to yield high azimuth resolution of a target in short coherent processing interval, which brings certain challenges for the subsequent three-dimensional (3-D) interference inverse synthetic aperture radar (InISAR) imaging. Aiming at this problem, this study introduces a novel 3-D InISAR imaging algorithm for the target with limited pluses. It is assumed that the received echoes have been compensated and image co-registered, and the variational Bayesian interference can be used to process the received limited pulses to yield the high-resolution 2-D ISAR images. At last, the spatial 3-D image of the target can be reconstructed after combining the interferometric processing and range measurement. The simulation results substantiate the validity of the presented algorithm.

Dynamic ISAR Imaging and Autofocusing of Maneuvering Targets Based on Sequential GP-SOONE Method and Eigenvalue Decomposition

A long coherent processing interval (CPI) is needed for achieving a high-resolution inverse synthetic aperture radar (ISAR) image. However, for a maneuvering target, the time-varying Doppler shifts cause a blurring effect on the ISAR image. Sparse representation-based algorithms can obtain a high-resolution image in a short CPI, while the Doppler shifts remain constant. Recently, a sequential order one negative exponential (SOONE) function has been introduced to measure the sparsity, and a gradient projection (GP) method has been used to solve the SOONE function and recover the sparse signal. In this paper, a 2D sequential GP-SOONE method for sparse recovery and dynamic ISAR imaging is proposed, which has a lower computational complexity than that of the 2D-GP-SOONE algorithm. Moreover, the performance of the proposed approach is the same as the 2D-GP-SOONE and better than the 2D smoothed L0 algorithm. Another problem of dynamic ISAR imaging is sequentially autofocusing the image. Hence, a fast parametric method based on eigenvalue decomposition and minimum entropy for dynamic ISAR autofocusing is proposed which has a faster convergence than the conventional methods. The proposed method has also comparable performance with the conventional ones. Several simulations and real data are used to show the superiority of the proposed methods.

Sparse Coding-Inspired High-Resolution ISAR Imaging Using Multistage Compressive Sensing

Sparse coding-inspired high-resolution inverse synthetic aperture radar (ISAR) imaging based on multistage compressive sensing (CS) is proposed in this paper. To achieve high cross-range resolution within a short coherent processing interval (CPI), we divide the ISAR imaging process into multiple stages, each of which can be regarded as sparse-coding processing, and the encoding and decoding matrices can be obtained by solving an optimization problem using the error backpropagation algorithm. The decoding matrix can be regarded as a sparse dictionary, and we can recover the ISAR image by exploiting the smoothed ${l_0}$ norm algorithm based on the decoding matrix and CS theory. The ISAR image is then transformed into a time-domain echo by the inverse fast Fourier transform, and the echo can be regarded as the input for the next stage. At each stage, the resolution is gradually improved until we obtain the expected high-resolution ISAR image. Thus, the signals' energy can be fully accumulated step by step, and we can obtain a less noise and focused ISAR image. Experimental results show that the proposed method can obtain higher quality ISAR images compared with other current techniques, and that it is an effective approach to ISAR imaging within a short CPI.

Dynamic ISAR imaging of maneuvering targets based on sparse matrix recovery

For high resolution inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, the Doppler frequency shifts are time varying during the coherent processing interval (CPI). Thus, the conventional range Doppler (RD) ISAR technique does not work properly. By exploiting two-dimensional (2D) sparsity of the target scene, 2D sparse matrix recovery algorithms are applied to achieve super-resolution within a short CPI, during which the Doppler shifts nearly remains constant. Sequential order one negative exponential (SOONE) function is used to measure the sparsity of a 2D signal. A 2D gradient projection (GP) method is developed to solve the SOONE function and thus the 2D-GP-SOONE algorithm is proposed. The algorithm can solve the sparse recovery of 2D signals directly. Then the 2D-GP-SOONE algorithm is used for the dynamic ISAR imaging of maneuvering targets. Theoretical analysis and simulation results show that the proposed method has a lower computational complexity and can achieve the fast recovering of a sparse matrix. Moreover, the proposed method has a better performance in ISAR imaging of maneuvering targets.

High-resolution ISAR imaging of maneuvering targets based on the sparse representation of multiple column-sparse vectors

For inverse synthetic aperture radar (ISAR) imaging of a maneuvering target, the time-variance of Doppler shifts will produce blurred images over a long coherent processing interval (CPI). Compressed sensing (CS) theory indicates that the precise recovery of an unknown sparse signal can be achieved from a very limited number of measurements. A novel sparse reconstruction of multiple column-sparse vectors using an algorithm with a minor reconstruction error and low computational complexity is proposed here based on multiple measurement vectors (MMV) and the smoothed ℓ0 norm (SL0) algorithm. For ISAR imaging of maneuvering targets, the Doppler shifts remain nearly constant for a short CPI. The proposed algorithm can produce high resolution ISAR images of maneuvering targets with an extremely limited number of pulses. Simulation results demonstrate the effectiveness and computational efficiency of the proposed method.

A novel strategy for inverse synthetic aperture radar imaging based on improved compressive sensing

In actual applications of inverse synthetic aperture radar (ISAR), continuous measurements may be impossible or the collection of data during some periods are not valid in a long coherent processing interval (CPI). Hence, it is significant to study the ISAR imaging strategy with a short CPI. Compressive sensing is a recently proposed technique that allows recovering an unknown sparse signal with overwhelming probability from very limited samples. However, the standard compressive sensing framework has been developed for real‐valued signals. One disadvantage of this method is that any prior phase information is not exploited, which may improve the reconstruction quality by applying some extra constraints. In this paper, a new strategy for ISAR imaging based on improved compressive sensing is proposed, which transforms the ISAR imaging problem into a joint optimization problem over the representation of the magnitude and phase of the complex‐valued scatter coefficient. Because of using phase information in the algorithm, the image quality is improved. Experimental results confirm the effectiveness of the proposed method. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

Dynamic ISAR Imaging of Maneuvering Targets Based on Sequential SL0

For maneuvering targets, the time-varying Doppler shifts will produce blurred inverse synthetic aperture radar (ISAR) images for a long coherent processing interval (CPI). By exploiting sparsity of the target scene, sparse recovery (SR) algorithms have been applied to achieve high cross-range resolution within a short CPI, during which the Doppler shifts nearly remain constant. For practical applications, however, the required pulse number for attaining an acceptable image is difficult to designate in various scenarios, and the common recovery procedure suffers from low efficiency because of having to solve a new SR problem from scratch when the new echo pulses are sequentially available. In this letter, we present a dynamic ISAR imaging algorithm based on sequential smoothed L0, which is proposed as an efficient recursive implementation of the SR approach. Furthermore, by defining the proper stopping rules, we can seek the optimal pulse number required in each CPI. Simulation results show that the proposed dynamic algorithm is more suitable for ISAR imaging of uncooperative targets.

Superresolution versus Motion Compensation-Based Techniques for Radar Imaging Defense Applications

Radar imaging of noncooperative targets is an interesting application of all-weather high-resolution coherent radars. However, these images are usually blurred when using the standard range-Doppler algorithm, if a long coherent processing interval (CPI) is used, and motion compensation techniques are hence necessary to improve imaging quality. If the CPI is reduced enough, target scatterers do not migrate of resolution cells and their corresponding Doppler frequencies are constant. Hence, for a short CPI, motion compensation is not longer necessary, but Doppler resolution gets degraded. In that case, superresolution algorithms may be applied. Here, we compare the superresolution-based focusing techniques with motion compensation-based methods. Our conclusion is that imaging quality after employing the superresolution approaches is not improved and, consequently, the use of motion compensation-based approaches to focus the radar images cannot be circumvented. Simulated and real data from high-resolution radars have been used to make the comparisons.